The amount of biological information generated in the today's world is increasing dramatically. It is estimated that the amount of information now doubles every four to five years. Because of the large amount of information that must be processed and analyzed, traditional methods of discerning and understanding the meaning of information, especially in the life science-related areas, are breaking down. Statistical techniques, while useful, do not provide a biologically motivated explanation of how things work. The present invention takes a causative approach (rather than correlative) at understanding biological effects.
To form an effective understanding of a biological system, a life science researcher must synthesize information from many sources. Understanding biological systems is made more difficult by the interdisciplinary nature of the life sciences. Forming an understanding of a biological system may require in-depth knowledge of genetics, cell biology, biochemistry, medicine, and many other fields. Understanding a system may require that information of many different types be combined. Life science information may include material on basic chemistry, proteins, cells, tissues, and effects on organisms or population—all of which may be interrelated. These interrelations may be complex, poorly understood, or hidden.
There are ongoing attempts to produce electronic models of biological systems. These involve compilation and organization of enormous amounts of data, and construction of a system that can operate on the data to simulate the behavior of a biological system. Because of the complexity of biology, and the sheer numbers of data, the construction of such a system can take hundreds of man years and multiple tens of millions of dollars. Furthermore, those seeking new insights and new knowledge in the life sciences are presented with the ever more difficult task of connecting the right data from mountains of information gleaned from vastly different sources. Companies willing to invest such resources so far have been unsuccessful in compiling models of real utility which aid researchers significantly in advancing biological knowledge. Thus, to the extent current systems of generating and recording life science data have been developed to permit knowledge processing and analysis, they are clearly far from optimal, and significant new efficiencies are needed.
More specifically, what is needed in the art is a way to assemble vast amounts of diverse life science-related knowledge, and to discern from it insightful and meaningful new biological relationships, pathways, causes and effects, and other insights with efficiency and ease.